Defect diagnosis technology based on multi-spectral point cloud

2021 ◽  
Author(s):  
Yang Yang ◽  
Ning Yang ◽  
Lihua Li ◽  
Fei Gao
Keyword(s):  
Point Cloud ◽  
Spectral Point ◽  
Defect Diagnosis

Graph Spectral Point Cloud Processing

2021 ◽  
pp. 181-219
Author(s):  
Wei Hu ◽  
Siheng Chen ◽  
Dong Tian
Keyword(s):  
Point Cloud ◽  
Cloud Processing ◽  
Spectral Point ◽  
Point Cloud Processing

scholarly journals Patch-Collaborative Spectral Point-Cloud Denoising

2013 ◽  
Vol 32 (8) ◽  
pp. 1-12 ◽  
Author(s):  
G. Rosman ◽  
A. Dubrovina ◽  
R. Kimmel
Keyword(s):  
Point Cloud ◽  
Spectral Point

A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto
Keyword(s):  
Point Cloud ◽  
Multi Scale ◽  
Feature Based ◽  
Shell Region

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Hierarchical point cloud denoising algorithm

2020 ◽  
Vol 28 (7) ◽  
pp. 1618-1625
Author(s):  
Fu-qun ZHAO ◽  
◽  
Keyword(s):  
Point Cloud

From Point Cloud to Triangular Mesh by Growing Neural Gas

2014 ◽  
Vol 24 (3) ◽  
pp. 651-662
Author(s):  
Feng ZENG ◽  
Tong YANG ◽  
Shan YAO
Keyword(s):  
Point Cloud ◽  
Triangular Mesh ◽  
Growing Neural Gas ◽  
Neural Gas

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm
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